<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.10.0" />
<title>silk.backbones.loftr.resnet_fpn API documentation</title>
<meta name="description" content="" />
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>silk.backbones.loftr.resnet_fpn</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

# source : https://github.com/zju3dv/LoFTR/blob/2122156015b61fbb650e28b58a958e4d632b1058/src/loftr/backbone/resnet_fpn.py

import torch.nn as nn
import torch.nn.functional as F
from silk.backbones.silk.coords import (
    LinearCoordinateMapping,
    mapping_from_torch_module,
    CoordinateMappingProvider,
    Identity,
)


def conv1x1(in_planes, out_planes, stride=1):
    &#34;&#34;&#34;1x1 convolution without padding&#34;&#34;&#34;
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=1,
        stride=stride,
        padding=0,
        bias=False,
    )


def conv3x3(in_planes, out_planes, stride=1, padding=1):
    &#34;&#34;&#34;3x3 convolution with padding&#34;&#34;&#34;
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=padding,
        bias=False,
    )


class RemovePad(nn.Module, CoordinateMappingProvider):
    def __init__(self, pad) -&gt; None:
        super().__init__()
        self._pad = pad

    def forward(self, x):
        if self._pad &gt; 0:
            return x[..., self._pad : -self._pad, self._pad : -self._pad]  # noqa: E203
        return x

    def mappings(self):
        return LinearCoordinateMapping(bias=-self._pad)


class BasicBlock(nn.Module, CoordinateMappingProvider):
    def __init__(self, in_planes, planes, stride=1, padding=1):
        super().__init__()
        self.conv1 = conv3x3(in_planes, planes, stride, padding=padding)
        self.conv2 = conv3x3(planes, planes, padding=padding)
        self.bn1 = nn.BatchNorm2d(planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)

        assert padding in {0, 1}

        pad = 1 - padding
        if stride == 1:
            self.downsample = RemovePad(2 * pad)
        else:
            self.downsample = nn.Sequential(
                RemovePad(3 * pad),
                conv1x1(in_planes, planes, stride=stride),
                nn.BatchNorm2d(planes),
            )

    def mappings(self):
        mapping = Identity()
        mapping = mapping + mapping_from_torch_module(self.conv1)
        mapping = mapping + mapping_from_torch_module(self.bn1)
        mapping = mapping + mapping_from_torch_module(self.relu)
        mapping = mapping + mapping_from_torch_module(self.conv2)
        mapping = mapping + mapping_from_torch_module(self.bn2)
        mapping = mapping + mapping_from_torch_module(self.relu)
        return mapping

    def forward(self, x):
        y = x
        y = self.relu(self.bn1(self.conv1(y)))
        y = self.bn2(self.conv2(y))

        x = self.downsample(x)

        return self.relu(x + y)


class ResNetFPN_8_2(nn.Module, CoordinateMappingProvider):
    &#34;&#34;&#34;
    ResNet+FPN, output resolution are 1/8 and 1/2.
    Each block has 2 layers.
    &#34;&#34;&#34;

    def __init__(self, config):
        super().__init__()

        # Config
        block = BasicBlock
        initial_dim = config[&#34;initial_dim&#34;]
        block_dims = config[&#34;block_dims&#34;]
        in_channels = config[&#34;in_channels&#34;]
        resolution_preserving = config.get(&#34;resolution_preserving&#34;, False)
        # padding = config.get(&#34;resolution_preserving&#34;, 1)
        padding = config.get(&#34;padding&#34;, 1)

        assert padding in {0, 1}

        # Class Variable
        self.in_planes = initial_dim
        self._padding = padding

        # Networks
        self._initial_stride = 1 if resolution_preserving else 2
        self._initial_padding = (
            3 * self._padding
        )  # &#34;same&#34; if resolution_preserving else 3
        self.conv1 = nn.Conv2d(
            in_channels,
            initial_dim,
            kernel_size=7,
            stride=self._initial_stride,
            padding=self._initial_padding,
            bias=False,
        )
        self.bn1 = nn.BatchNorm2d(initial_dim)
        self.relu = nn.ReLU(inplace=True)

        self.layer1 = self._make_layer(
            block,
            block_dims[0],
            stride=1,
            padding=self._padding,
        )  # 1/2
        self.layer2 = self._make_layer(
            block,
            block_dims[1],
            stride=2,
            padding=self._padding,
        )  # 1/4
        self.layer3 = self._make_layer(
            block,
            block_dims[2],
            stride=2,
            padding=self._padding,
        )  # 1/8

        # 3. FPN upsample
        self.layer3_outconv = conv1x1(block_dims[2], block_dims[2])
        self.layer2_outconv = conv1x1(block_dims[1], block_dims[2])
        self.layer2_outconv2 = nn.Sequential(
            conv3x3(block_dims[2], block_dims[2], padding=padding),
            nn.BatchNorm2d(block_dims[2]),
            nn.LeakyReLU(),
            conv3x3(block_dims[2], block_dims[1], padding=padding),
        )
        self.layer1_outconv = conv1x1(block_dims[0], block_dims[1])
        self.layer1_outconv2 = nn.Sequential(
            conv3x3(block_dims[1], block_dims[1], padding=padding),
            nn.BatchNorm2d(block_dims[1]),
            nn.LeakyReLU(),
            conv3x3(block_dims[1], block_dims[0], padding=padding),
        )

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode=&#34;fan_out&#34;, nonlinearity=&#34;relu&#34;)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, dim, stride=1, padding=1):
        layer1 = block(self.in_planes, dim, stride=stride, padding=padding)
        layer2 = block(dim, dim, stride=1, padding=padding)
        layers = (layer1, layer2)

        self.in_planes = dim
        return nn.Sequential(*layers)

    def mappings(self):
        mapping = Identity()
        mapping = mapping + mapping_from_torch_module(self.conv1)
        mapping = mapping + mapping_from_torch_module(self.bn1)
        mapping = mapping + mapping_from_torch_module(self.relu)
        mapping = mapping + mapping_from_torch_module(self.layer1)
        mapping_x1 = mapping
        mapping = mapping + mapping_from_torch_module(self.layer2)
        mapping = mapping + mapping_from_torch_module(self.layer3)
        mapping = mapping + mapping_from_torch_module(self.layer3_outconv)
        mapping_x3 = mapping
        mapping_x1 = mapping_x1 + mapping_from_torch_module(self.layer1_outconv)
        mapping_x1 = mapping_x1 + mapping_from_torch_module(self.layer1_outconv2)

        return mapping_x3, mapping_x1

    def forward(self, x):
        # ResNet Backbone
        x0 = self.relu(self.bn1(self.conv1(x)))
        x1 = self.layer1(x0)  # 1/2
        x2 = self.layer2(x1)  # 1/4
        x3 = self.layer3(x2)  # 1/8

        # FPN
        x3_out = self.layer3_outconv(x3)

        x3_out_2x = F.interpolate(
            x3_out,
            size=x2.shape[2:],
            mode=&#34;bilinear&#34;,
            align_corners=True,
        )
        x2_out = self.layer2_outconv(x2)
        x2_out = self.layer2_outconv2(x2_out + x3_out_2x)

        x2_out_2x = F.interpolate(
            x2_out,
            size=x1.shape[2:],
            mode=&#34;bilinear&#34;,
            align_corners=True,
        )
        x1_out = self.layer1_outconv(x1)
        x1_out = self.layer1_outconv2(x1_out + x2_out_2x)

        return [x3_out, x1_out]


class ResNetFPN_16_4(nn.Module):
    &#34;&#34;&#34;
    ResNet+FPN, output resolution are 1/16 and 1/4.
    Each block has 2 layers.
    &#34;&#34;&#34;

    def __init__(self, config):
        super().__init__()
        # Config
        block = BasicBlock
        initial_dim = config[&#34;initial_dim&#34;]
        block_dims = config[&#34;block_dims&#34;]

        # Class Variable
        self.in_planes = initial_dim

        # Networks
        self.conv1 = nn.Conv2d(
            1,
            initial_dim,
            kernel_size=7,
            stride=2,
            padding=3,
            bias=False,
        )
        self.bn1 = nn.BatchNorm2d(initial_dim)
        self.relu = nn.ReLU(inplace=True)

        self.layer1 = self._make_layer(block, block_dims[0], stride=1)  # 1/2
        self.layer2 = self._make_layer(block, block_dims[1], stride=2)  # 1/4
        self.layer3 = self._make_layer(block, block_dims[2], stride=2)  # 1/8
        self.layer4 = self._make_layer(block, block_dims[3], stride=2)  # 1/16

        # 3. FPN upsample
        self.layer4_outconv = conv1x1(block_dims[3], block_dims[3])
        self.layer3_outconv = conv1x1(block_dims[2], block_dims[3])
        self.layer3_outconv2 = nn.Sequential(
            conv3x3(block_dims[3], block_dims[3]),
            nn.BatchNorm2d(block_dims[3]),
            nn.LeakyReLU(),
            conv3x3(block_dims[3], block_dims[2]),
        )

        self.layer2_outconv = conv1x1(block_dims[1], block_dims[2])
        self.layer2_outconv2 = nn.Sequential(
            conv3x3(block_dims[2], block_dims[2]),
            nn.BatchNorm2d(block_dims[2]),
            nn.LeakyReLU(),
            conv3x3(block_dims[2], block_dims[1]),
        )

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode=&#34;fan_out&#34;, nonlinearity=&#34;relu&#34;)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, dim, stride=1):
        layer1 = block(self.in_planes, dim, stride=stride)
        layer2 = block(dim, dim, stride=1)
        layers = (layer1, layer2)

        self.in_planes = dim
        return nn.Sequential(*layers)

    def forward(self, x):
        # ResNet Backbone
        x0 = self.relu(self.bn1(self.conv1(x)))
        x1 = self.layer1(x0)  # 1/2
        x2 = self.layer2(x1)  # 1/4
        x3 = self.layer3(x2)  # 1/8
        x4 = self.layer4(x3)  # 1/16

        # FPN
        x4_out = self.layer4_outconv(x4)

        x4_out_2x = F.interpolate(
            x4_out,
            scale_factor=2.0,
            mode=&#34;bilinear&#34;,
            align_corners=True,
        )
        x3_out = self.layer3_outconv(x3)
        x3_out = self.layer3_outconv2(x3_out + x4_out_2x)

        x3_out_2x = F.interpolate(
            x3_out,
            scale_factor=2.0,
            mode=&#34;bilinear&#34;,
            align_corners=True,
        )
        x2_out = self.layer2_outconv(x2)
        x2_out = self.layer2_outconv2(x2_out + x3_out_2x)

        return [x4_out, x2_out]</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="silk.backbones.loftr.resnet_fpn.conv1x1"><code class="name flex">
<span>def <span class="ident">conv1x1</span></span>(<span>in_planes, out_planes, stride=1)</span>
</code></dt>
<dd>
<div class="desc"><p>1x1 convolution without padding</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def conv1x1(in_planes, out_planes, stride=1):
    &#34;&#34;&#34;1x1 convolution without padding&#34;&#34;&#34;
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=1,
        stride=stride,
        padding=0,
        bias=False,
    )</code></pre>
</details>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.conv3x3"><code class="name flex">
<span>def <span class="ident">conv3x3</span></span>(<span>in_planes, out_planes, stride=1, padding=1)</span>
</code></dt>
<dd>
<div class="desc"><p>3x3 convolution with padding</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def conv3x3(in_planes, out_planes, stride=1, padding=1):
    &#34;&#34;&#34;3x3 convolution with padding&#34;&#34;&#34;
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=padding,
        bias=False,
    )</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.backbones.loftr.resnet_fpn.BasicBlock"><code class="flex name class">
<span>class <span class="ident">BasicBlock</span></span>
<span>(</span><span>in_planes, planes, stride=1, padding=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class BasicBlock(nn.Module, CoordinateMappingProvider):
    def __init__(self, in_planes, planes, stride=1, padding=1):
        super().__init__()
        self.conv1 = conv3x3(in_planes, planes, stride, padding=padding)
        self.conv2 = conv3x3(planes, planes, padding=padding)
        self.bn1 = nn.BatchNorm2d(planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)

        assert padding in {0, 1}

        pad = 1 - padding
        if stride == 1:
            self.downsample = RemovePad(2 * pad)
        else:
            self.downsample = nn.Sequential(
                RemovePad(3 * pad),
                conv1x1(in_planes, planes, stride=stride),
                nn.BatchNorm2d(planes),
            )

    def mappings(self):
        mapping = Identity()
        mapping = mapping + mapping_from_torch_module(self.conv1)
        mapping = mapping + mapping_from_torch_module(self.bn1)
        mapping = mapping + mapping_from_torch_module(self.relu)
        mapping = mapping + mapping_from_torch_module(self.conv2)
        mapping = mapping + mapping_from_torch_module(self.bn2)
        mapping = mapping + mapping_from_torch_module(self.relu)
        return mapping

    def forward(self, x):
        y = x
        y = self.relu(self.bn1(self.conv1(y)))
        y = self.bn2(self.conv2(y))

        x = self.downsample(x)

        return self.relu(x + y)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
<li><a title="silk.backbones.silk.coords.CoordinateMappingProvider" href="../silk/coords.html#silk.backbones.silk.coords.CoordinateMappingProvider">CoordinateMappingProvider</a></li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.loftr.resnet_fpn.BasicBlock.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.BasicBlock.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.loftr.resnet_fpn.BasicBlock.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the :class:<code>Module</code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, x):
    y = x
    y = self.relu(self.bn1(self.conv1(y)))
    y = self.bn2(self.conv2(y))

    x = self.downsample(x)

    return self.relu(x + y)</code></pre>
</details>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.BasicBlock.mappings"><code class="name flex">
<span>def <span class="ident">mappings</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def mappings(self):
    mapping = Identity()
    mapping = mapping + mapping_from_torch_module(self.conv1)
    mapping = mapping + mapping_from_torch_module(self.bn1)
    mapping = mapping + mapping_from_torch_module(self.relu)
    mapping = mapping + mapping_from_torch_module(self.conv2)
    mapping = mapping + mapping_from_torch_module(self.bn2)
    mapping = mapping + mapping_from_torch_module(self.relu)
    return mapping</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.RemovePad"><code class="flex name class">
<span>class <span class="ident">RemovePad</span></span>
<span>(</span><span>pad)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class RemovePad(nn.Module, CoordinateMappingProvider):
    def __init__(self, pad) -&gt; None:
        super().__init__()
        self._pad = pad

    def forward(self, x):
        if self._pad &gt; 0:
            return x[..., self._pad : -self._pad, self._pad : -self._pad]  # noqa: E203
        return x

    def mappings(self):
        return LinearCoordinateMapping(bias=-self._pad)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
<li><a title="silk.backbones.silk.coords.CoordinateMappingProvider" href="../silk/coords.html#silk.backbones.silk.coords.CoordinateMappingProvider">CoordinateMappingProvider</a></li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.loftr.resnet_fpn.RemovePad.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.RemovePad.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.loftr.resnet_fpn.RemovePad.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the :class:<code>Module</code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, x):
    if self._pad &gt; 0:
        return x[..., self._pad : -self._pad, self._pad : -self._pad]  # noqa: E203
    return x</code></pre>
</details>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.RemovePad.mappings"><code class="name flex">
<span>def <span class="ident">mappings</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def mappings(self):
    return LinearCoordinateMapping(bias=-self._pad)</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4"><code class="flex name class">
<span>class <span class="ident">ResNetFPN_16_4</span></span>
<span>(</span><span>config)</span>
</code></dt>
<dd>
<div class="desc"><p>ResNet+FPN, output resolution are 1/16 and 1/4.
Each block has 2 layers.</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ResNetFPN_16_4(nn.Module):
    &#34;&#34;&#34;
    ResNet+FPN, output resolution are 1/16 and 1/4.
    Each block has 2 layers.
    &#34;&#34;&#34;

    def __init__(self, config):
        super().__init__()
        # Config
        block = BasicBlock
        initial_dim = config[&#34;initial_dim&#34;]
        block_dims = config[&#34;block_dims&#34;]

        # Class Variable
        self.in_planes = initial_dim

        # Networks
        self.conv1 = nn.Conv2d(
            1,
            initial_dim,
            kernel_size=7,
            stride=2,
            padding=3,
            bias=False,
        )
        self.bn1 = nn.BatchNorm2d(initial_dim)
        self.relu = nn.ReLU(inplace=True)

        self.layer1 = self._make_layer(block, block_dims[0], stride=1)  # 1/2
        self.layer2 = self._make_layer(block, block_dims[1], stride=2)  # 1/4
        self.layer3 = self._make_layer(block, block_dims[2], stride=2)  # 1/8
        self.layer4 = self._make_layer(block, block_dims[3], stride=2)  # 1/16

        # 3. FPN upsample
        self.layer4_outconv = conv1x1(block_dims[3], block_dims[3])
        self.layer3_outconv = conv1x1(block_dims[2], block_dims[3])
        self.layer3_outconv2 = nn.Sequential(
            conv3x3(block_dims[3], block_dims[3]),
            nn.BatchNorm2d(block_dims[3]),
            nn.LeakyReLU(),
            conv3x3(block_dims[3], block_dims[2]),
        )

        self.layer2_outconv = conv1x1(block_dims[1], block_dims[2])
        self.layer2_outconv2 = nn.Sequential(
            conv3x3(block_dims[2], block_dims[2]),
            nn.BatchNorm2d(block_dims[2]),
            nn.LeakyReLU(),
            conv3x3(block_dims[2], block_dims[1]),
        )

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode=&#34;fan_out&#34;, nonlinearity=&#34;relu&#34;)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, dim, stride=1):
        layer1 = block(self.in_planes, dim, stride=stride)
        layer2 = block(dim, dim, stride=1)
        layers = (layer1, layer2)

        self.in_planes = dim
        return nn.Sequential(*layers)

    def forward(self, x):
        # ResNet Backbone
        x0 = self.relu(self.bn1(self.conv1(x)))
        x1 = self.layer1(x0)  # 1/2
        x2 = self.layer2(x1)  # 1/4
        x3 = self.layer3(x2)  # 1/8
        x4 = self.layer4(x3)  # 1/16

        # FPN
        x4_out = self.layer4_outconv(x4)

        x4_out_2x = F.interpolate(
            x4_out,
            scale_factor=2.0,
            mode=&#34;bilinear&#34;,
            align_corners=True,
        )
        x3_out = self.layer3_outconv(x3)
        x3_out = self.layer3_outconv2(x3_out + x4_out_2x)

        x3_out_2x = F.interpolate(
            x3_out,
            scale_factor=2.0,
            mode=&#34;bilinear&#34;,
            align_corners=True,
        )
        x2_out = self.layer2_outconv(x2)
        x2_out = self.layer2_outconv2(x2_out + x3_out_2x)

        return [x4_out, x2_out]</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the :class:<code>Module</code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, x):
    # ResNet Backbone
    x0 = self.relu(self.bn1(self.conv1(x)))
    x1 = self.layer1(x0)  # 1/2
    x2 = self.layer2(x1)  # 1/4
    x3 = self.layer3(x2)  # 1/8
    x4 = self.layer4(x3)  # 1/16

    # FPN
    x4_out = self.layer4_outconv(x4)

    x4_out_2x = F.interpolate(
        x4_out,
        scale_factor=2.0,
        mode=&#34;bilinear&#34;,
        align_corners=True,
    )
    x3_out = self.layer3_outconv(x3)
    x3_out = self.layer3_outconv2(x3_out + x4_out_2x)

    x3_out_2x = F.interpolate(
        x3_out,
        scale_factor=2.0,
        mode=&#34;bilinear&#34;,
        align_corners=True,
    )
    x2_out = self.layer2_outconv(x2)
    x2_out = self.layer2_outconv2(x2_out + x3_out_2x)

    return [x4_out, x2_out]</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2"><code class="flex name class">
<span>class <span class="ident">ResNetFPN_8_2</span></span>
<span>(</span><span>config)</span>
</code></dt>
<dd>
<div class="desc"><p>ResNet+FPN, output resolution are 1/8 and 1/2.
Each block has 2 layers.</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ResNetFPN_8_2(nn.Module, CoordinateMappingProvider):
    &#34;&#34;&#34;
    ResNet+FPN, output resolution are 1/8 and 1/2.
    Each block has 2 layers.
    &#34;&#34;&#34;

    def __init__(self, config):
        super().__init__()

        # Config
        block = BasicBlock
        initial_dim = config[&#34;initial_dim&#34;]
        block_dims = config[&#34;block_dims&#34;]
        in_channels = config[&#34;in_channels&#34;]
        resolution_preserving = config.get(&#34;resolution_preserving&#34;, False)
        # padding = config.get(&#34;resolution_preserving&#34;, 1)
        padding = config.get(&#34;padding&#34;, 1)

        assert padding in {0, 1}

        # Class Variable
        self.in_planes = initial_dim
        self._padding = padding

        # Networks
        self._initial_stride = 1 if resolution_preserving else 2
        self._initial_padding = (
            3 * self._padding
        )  # &#34;same&#34; if resolution_preserving else 3
        self.conv1 = nn.Conv2d(
            in_channels,
            initial_dim,
            kernel_size=7,
            stride=self._initial_stride,
            padding=self._initial_padding,
            bias=False,
        )
        self.bn1 = nn.BatchNorm2d(initial_dim)
        self.relu = nn.ReLU(inplace=True)

        self.layer1 = self._make_layer(
            block,
            block_dims[0],
            stride=1,
            padding=self._padding,
        )  # 1/2
        self.layer2 = self._make_layer(
            block,
            block_dims[1],
            stride=2,
            padding=self._padding,
        )  # 1/4
        self.layer3 = self._make_layer(
            block,
            block_dims[2],
            stride=2,
            padding=self._padding,
        )  # 1/8

        # 3. FPN upsample
        self.layer3_outconv = conv1x1(block_dims[2], block_dims[2])
        self.layer2_outconv = conv1x1(block_dims[1], block_dims[2])
        self.layer2_outconv2 = nn.Sequential(
            conv3x3(block_dims[2], block_dims[2], padding=padding),
            nn.BatchNorm2d(block_dims[2]),
            nn.LeakyReLU(),
            conv3x3(block_dims[2], block_dims[1], padding=padding),
        )
        self.layer1_outconv = conv1x1(block_dims[0], block_dims[1])
        self.layer1_outconv2 = nn.Sequential(
            conv3x3(block_dims[1], block_dims[1], padding=padding),
            nn.BatchNorm2d(block_dims[1]),
            nn.LeakyReLU(),
            conv3x3(block_dims[1], block_dims[0], padding=padding),
        )

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode=&#34;fan_out&#34;, nonlinearity=&#34;relu&#34;)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, dim, stride=1, padding=1):
        layer1 = block(self.in_planes, dim, stride=stride, padding=padding)
        layer2 = block(dim, dim, stride=1, padding=padding)
        layers = (layer1, layer2)

        self.in_planes = dim
        return nn.Sequential(*layers)

    def mappings(self):
        mapping = Identity()
        mapping = mapping + mapping_from_torch_module(self.conv1)
        mapping = mapping + mapping_from_torch_module(self.bn1)
        mapping = mapping + mapping_from_torch_module(self.relu)
        mapping = mapping + mapping_from_torch_module(self.layer1)
        mapping_x1 = mapping
        mapping = mapping + mapping_from_torch_module(self.layer2)
        mapping = mapping + mapping_from_torch_module(self.layer3)
        mapping = mapping + mapping_from_torch_module(self.layer3_outconv)
        mapping_x3 = mapping
        mapping_x1 = mapping_x1 + mapping_from_torch_module(self.layer1_outconv)
        mapping_x1 = mapping_x1 + mapping_from_torch_module(self.layer1_outconv2)

        return mapping_x3, mapping_x1

    def forward(self, x):
        # ResNet Backbone
        x0 = self.relu(self.bn1(self.conv1(x)))
        x1 = self.layer1(x0)  # 1/2
        x2 = self.layer2(x1)  # 1/4
        x3 = self.layer3(x2)  # 1/8

        # FPN
        x3_out = self.layer3_outconv(x3)

        x3_out_2x = F.interpolate(
            x3_out,
            size=x2.shape[2:],
            mode=&#34;bilinear&#34;,
            align_corners=True,
        )
        x2_out = self.layer2_outconv(x2)
        x2_out = self.layer2_outconv2(x2_out + x3_out_2x)

        x2_out_2x = F.interpolate(
            x2_out,
            size=x1.shape[2:],
            mode=&#34;bilinear&#34;,
            align_corners=True,
        )
        x1_out = self.layer1_outconv(x1)
        x1_out = self.layer1_outconv2(x1_out + x2_out_2x)

        return [x3_out, x1_out]</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>torch.nn.modules.module.Module</li>
<li><a title="silk.backbones.silk.coords.CoordinateMappingProvider" href="../silk/coords.html#silk.backbones.silk.coords.CoordinateMappingProvider">CoordinateMappingProvider</a></li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, x) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the :class:<code>Module</code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, x):
    # ResNet Backbone
    x0 = self.relu(self.bn1(self.conv1(x)))
    x1 = self.layer1(x0)  # 1/2
    x2 = self.layer2(x1)  # 1/4
    x3 = self.layer3(x2)  # 1/8

    # FPN
    x3_out = self.layer3_outconv(x3)

    x3_out_2x = F.interpolate(
        x3_out,
        size=x2.shape[2:],
        mode=&#34;bilinear&#34;,
        align_corners=True,
    )
    x2_out = self.layer2_outconv(x2)
    x2_out = self.layer2_outconv2(x2_out + x3_out_2x)

    x2_out_2x = F.interpolate(
        x2_out,
        size=x1.shape[2:],
        mode=&#34;bilinear&#34;,
        align_corners=True,
    )
    x1_out = self.layer1_outconv(x1)
    x1_out = self.layer1_outconv2(x1_out + x2_out_2x)

    return [x3_out, x1_out]</code></pre>
</details>
</dd>
<dt id="silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.mappings"><code class="name flex">
<span>def <span class="ident">mappings</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def mappings(self):
    mapping = Identity()
    mapping = mapping + mapping_from_torch_module(self.conv1)
    mapping = mapping + mapping_from_torch_module(self.bn1)
    mapping = mapping + mapping_from_torch_module(self.relu)
    mapping = mapping + mapping_from_torch_module(self.layer1)
    mapping_x1 = mapping
    mapping = mapping + mapping_from_torch_module(self.layer2)
    mapping = mapping + mapping_from_torch_module(self.layer3)
    mapping = mapping + mapping_from_torch_module(self.layer3_outconv)
    mapping_x3 = mapping
    mapping_x1 = mapping_x1 + mapping_from_torch_module(self.layer1_outconv)
    mapping_x1 = mapping_x1 + mapping_from_torch_module(self.layer1_outconv2)

    return mapping_x3, mapping_x1</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.backbones.loftr" href="index.html">silk.backbones.loftr</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="silk.backbones.loftr.resnet_fpn.conv1x1" href="#silk.backbones.loftr.resnet_fpn.conv1x1">conv1x1</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.conv3x3" href="#silk.backbones.loftr.resnet_fpn.conv3x3">conv3x3</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.backbones.loftr.resnet_fpn.BasicBlock" href="#silk.backbones.loftr.resnet_fpn.BasicBlock">BasicBlock</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.loftr.resnet_fpn.BasicBlock.dump_patches" href="#silk.backbones.loftr.resnet_fpn.BasicBlock.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.BasicBlock.forward" href="#silk.backbones.loftr.resnet_fpn.BasicBlock.forward">forward</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.BasicBlock.mappings" href="#silk.backbones.loftr.resnet_fpn.BasicBlock.mappings">mappings</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.BasicBlock.training" href="#silk.backbones.loftr.resnet_fpn.BasicBlock.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.backbones.loftr.resnet_fpn.RemovePad" href="#silk.backbones.loftr.resnet_fpn.RemovePad">RemovePad</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.loftr.resnet_fpn.RemovePad.dump_patches" href="#silk.backbones.loftr.resnet_fpn.RemovePad.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.RemovePad.forward" href="#silk.backbones.loftr.resnet_fpn.RemovePad.forward">forward</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.RemovePad.mappings" href="#silk.backbones.loftr.resnet_fpn.RemovePad.mappings">mappings</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.RemovePad.training" href="#silk.backbones.loftr.resnet_fpn.RemovePad.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4" href="#silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4">ResNetFPN_16_4</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4.dump_patches" href="#silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4.forward" href="#silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4.forward">forward</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4.training" href="#silk.backbones.loftr.resnet_fpn.ResNetFPN_16_4.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2" href="#silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2">ResNetFPN_8_2</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.dump_patches" href="#silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.forward" href="#silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.forward">forward</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.mappings" href="#silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.mappings">mappings</a></code></li>
<li><code><a title="silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.training" href="#silk.backbones.loftr.resnet_fpn.ResNetFPN_8_2.training">training</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
</footer>
</body>
</html>